abstract = "Heuristic policies for combinatorial optimisation
problems can be found by using Genetic programming (GP)
to evolve a mathematical function over variables given
by the current state of the problem, and whose value is
used to determine action choices (such as preferred
assignments or branches). If all variables have finite
discrete domains, then the expressions can be converted
to an equivalent lookup table or `decision matrix'.
Spaces of such matrices often have natural distance
metrics (after conversion to a standard form). As a
case study, and to support the understanding of GP as a
meta-heuristic, we extend previous bin-packing work and
compare the distances between matrices from before and
after a GP-driven mutation. We find that GP mutations
often correspond to large moves within the space of
decision matrices. This strengthens evidence that the
role of mutations within GP might be somewhat different
than their role within Genetic Algorithms.",

notes = "Automated development of heuristics for the bin
packing problem. Hyper-heuristics. ECJ. Part of
\cite{Moraglio:2012:GP}EuroGP'2012 held in conjunction
with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and
EvoApplications2012",